30 June 2010. Theories about the neurobiology underlying cognitive deficits in schizophrenia lead to inaccurate predictions about working memory impairments in the disorder, says a study in the June Archives of General Psychiatry. James Gold of the University of Maryland School of Medicine in Baltimore and colleagues questioned whether, as some theories suggest, working memory would be unstable and imprecise in schizophrenia. Using novel methods that enabled them to tease apart different aspects of working memory, they found no evidence that people with schizophrenia have more fleeting or off-the-mark memories than healthy subjects. Rather, they are unable to simultaneously hold as many items at once in working memory. In light of these findings, Gold et al. recommend a trip back to the drawing board to revise current theories of the neurobiology of working memory deficits in schizophrenia.

Schizophrenia-related cognitive deficits hamper patients’ lives (see Green, 1996), and a recent meta-analysis found consistent evidence of large working memory deficits in subjects with schizophrenia as compared to healthy subjects (Forbes et al., 2009). Working memory, the ability to briefly store and manipulate information, guides goal-directed behavior through other cognitive processes.

Several researchers have tried to explain the biological basis of cognitive symptoms in schizophrenia. For instance, John Lisman and colleagues (Lisman et al., 2008) suggest that reduced N-methyl-D-aspartate (NMDA) channel function impairs memory in schizophrenia by disinhibiting pyramidal cells in the hippocampus, thereby reducing gamma waves (see SRF related news story). Edmund Rolls and associates (Rolls et al., 2008) suggest that reduced dopamine in schizophrenia decreases NMDA currents, causing neural networks to randomly fire, adding noise that drowns out information-carrying signals, rendering the networks unstable. Daniel Durstewitz and Jeremy Seamans (2008) propose that imbalanced activation of dopamine D1 and D2 receptors in the prefrontal cortex may result in excess noise and overly frail representations in memory. These theories could lead one to expect imprecise or unstable working memory in schizophrenia.

Yet, when Gold and colleagues looked at previous findings regarding working memory in schizophrenia, they found reason to doubt that schizophrenia causes faster-decaying or less accurate memories. Members of the research team, Wei Zhang and Steven Luck, both of the University of California at Davis, had recently devised a way to separately examine the number of representations in memory, the precision of those memories, and their stability over time. This provided an opportunity for the research group to test current theories of deficits in the working memory of individuals with schizophrenia.

The researchers’ approach involved a case-control design in which the researchers tested the working memory of 31 clinically stable patients who met criteria for schizophrenia or schizoaffective disorder. They compared them with 26 mentally healthy subjects who had no history of psychosis. Controls mirrored the patients in age, sex, ethnicity, and parental education. All participants were presented with three or four different colors on a computer screen. After a pause when the screen went blank, subjects were to indicate the color shown in a particular spot by selecting and clicking on it on a color wheel. Subjects who stored the color in memory and recalled it when tested should select colors similar to those actually shown. Those who did not would have to guess.

By examining the distribution of errors, Gold and colleagues determined the probability that subjects held an item in memory at test time and the precision of that representation. To check the stability of working memory representations, they tried both a one-second and a four-second delay. They reasoned that if patients with schizophrenia have unstable memories, they should perform worse than control subjects after a short delay.

A mixed bag
The results suggest that schizophrenia reduces working memory capacity, causing subjects with schizophrenia to store fewer items. Even so, patients recalled items that had been stored in memory with the same precision as healthy subjects. Furthermore, the length of delay made no difference in either the number of items recalled or the precision of recall for either group, contrary to expectations of less stable memories in schizophrenia. “In our view, the recent biological accounts discussed above are at odds with much of the behavioral literature, and clearly at odds with the data presented here,” write Gold and associates.

While Gold et al. acknowledge that a longer delay might bring out unseen differences between the two groups of subjects, they think that a four-second delay should be sufficient for detecting the disruptive working memory deficits expected in schizophrenia. They cannot explain why the working memory of subjects with schizophrenia would hold fewer items, although they note that neuroimaging studies point to the parietal cortex, perhaps in league with the prefrontal cortex, in setting capacity for visual working memory. They write, “Unfortunately, there is very little understanding of the origins of capacity limits in the basic cognitive neuroscience literature.”

All of the patients in the study were undergoing treatment with antipsychotic medication, including clozapine in 18 cases, suggesting that other treatments had failed them (see SRF related news story). This indicates that clozapine may boost the signal-to-noise ratio (see SRF related news story). Gold and colleagues warn that untreated patients in the early stages of schizophrenia might show a different pattern of deficits. They also caution that the results might not extend to other working memory tasks that activate different neural pathways and/or do not involve color judgments.

Despite these findings, or maybe because of them, Gold and colleagues see “a great need” for models that explain the actual working memory deficits seen in schizophrenia. Even so, they write, “these models must accurately capture the behavioral endpoint, which is characterized primarily by reductions in storage capacity and not by an instability of the working memory representations.”—Victoria L. Wilcox.

Gold and colleagues have provided an extremely elegant example of how a precisely controlled behavioral study can be used to directly test implications generated by neurobiological theories of cognitive impairment in schizophrenia. Further, they have provided novel and important data in schizophrenia that should cause us to re-examine theories about the mechanisms underling working memory impairments in this illness.

As noted by Gold, it has been hypothesized that altered GABAergic, glutamatergic, and/or dopaminergic inputs into reverberating and oscillatory networks in prefrontal or parietal cortex among individuals with schizophrenia should render such networks unstable and lead to less precise working memory representations that are particularly prone to decay (Lisman et al., 2008; Durstewitz and Seamans, 2008; Rolls et al., 2008; Lewis et al., 2008). However, Gold and colleagues have shown that working memory representations in schizophrenia (at least of color memory) are neither less precise nor show evidence of exceptionally rapid decay. Instead, individuals with schizophrenia showed clearly reduced working memory capacity.

These data contribute to a systemic body of work generated by Gold and colleagues, who have investigated the many aspects of working memory that could be impaired in schizophrenia. They have also shown that iconic decay is not increased in schizophrenia (Hahn et al., 2010), that feature binding is intact (Gold et al., 2003), and that certain aspects of attentional control over working memory are intact (Gold et al., 2006), though others are impaired (Fuller et al., 2006). However, working memory capacity has consistently been shown to be reduced in schizophrenia across numerous studies (Gold et al., 2006; van Raalten et al., 2008; Silver et al., 2003). If we take these results seriously (and we should), they require us to look closely at the neural mechanisms postulated to modulate capacity limitations in working memory in order to generate clues to the mechanisms that may be leading to reduced working memory capacity in schizophrenia.

The neural mechanisms leading to working memory capacity limitations are still very much an open source of debate. However, one influential theory is that the number of â€śitemsâ€ť that can be maintained in working memory is limited by the number of gamma cycles (30-100 Hz) that can be embedded within a theta cycle (Lisman, 2010). Related to the idea that originally drove the design of the Gold study, Lisman and others have hypothesized that individual items within working memory are represented by oscillating neural populations with spike rates phase-locked in a gamma cycle. The oscillatory activity representing different items must be kept isolated, potentially by keeping gamma activity for different items out of phase with each other. One way to accomplish this would be to couple such gamma cycles into a lower frequency theta oscillation that can help regulate and separate activity associated with different items (as well as maintain information about order). Lisman and others have argued that capacity constraints of approximately four items in working memory (Cowan, 2001) thus reflect the number of gamma cycles that can be embedded in a theta cycle (approximately four) (Lisman, 2010; Wolters and Raffone, 2008).

Goldâ€™s results suggest that it may not be the maintenance of the individual gamma-oscillating neural populations representing individual items that is impaired in schizophrenia. Instead, it may be either the ability to establish such synchronous neural activity associated with a specific item, or the ability to couple a number of different gamma-oscillating sub-networks into a theta cycle. Interestingly, a growing number of studies have shown altered gamma activity during working memory in schizophrenia (Barr et al., 2010; Basar-Eroglu et al., 2007; Light et al., 2006; Kissler et al., 2000), as well as some evidence for altered theta activity (Haenschel et al., 2009). However, additional work is needed to specifically examine gamma-theta coupling in schizophrenia and its role in determining capacity limitations in this disease.

The type of network models of working memory put forth by Wang and colleagues suggest that the dynamics of excitatory and inhibitory inputs drive the number of independent â€śactivity bumpsâ€ť (i.e., items) that can be maintained in a network (Compte et al., 2000). A related idea about the mechanisms driving capacity limitations and variations in these limits across individuals has been put forth by Klingberg and colleagues, who have argued that the dynamics of such lateral inhibitory mechanisms in parietal cortex limit memory capacity to be between two and seven items (Edin et al., 2009). However, they have also argued that such capacity limits can be overcome, at least temporarily, by excitatory inputs into parietal cortex from prefrontal cortex (Edin et al., 2009). They have suggested that this provides a mechanistic account of top-down control over working memory capacity by prefrontal cortex. As such, given the evidence for at least some types of abnormalities in top-down control of attention in schizophrenia (Fuller et al., 2006; Hahn et al., 2010), and evidence for altered connectivity between prefrontal and parietal regions (Barch and Csernansky, 2007; Karlsgodt et al., 2008), another possible source of reduced capacity in working memory in schizophrenia may be a reduction in prefrontal-mediated excitatory input into parietal networks that maintain items in working memory.

One might argue that the same GABA, glutamate, or dopamine mechanisms thought to impair the maintenance of representations in working memory could also impair the initial establishment of gamma oscillating networks representing items, their coupling to a lower-frequency theta cycle, or even the ability of prefrontal cortex to provide excitatory inputs into neural networks supporting the representation of items in working memory. If so, such models will also need to explain how such impairments could lead to reduced working memory capacity in schizophrenia without a change in precision or decay, a challenge for most current neural network models of working memory. As such, the data provided by Gold and colleagues suggest an exciting new pathway for research on working memory in schizophrenia that may allow us to develop more precise mechanistic hypotheses as to the source of these cognitive impairments and their relationship to pathophysiology of this illness.

Schizophrenia is associated with dopaminergic dysfunction, impaired gamma synchronization and impaired methylation. It is therefore of interest that the D4 dopamine receptor is involved in gamma synchronization (Demiralp et al., 2006) and that the D4 dopamine receptor uniquely carries out methylation of membrane phospholipids (Sharma et al., 1999). A reasonable and unifying hypothesis would be that schizophrenia results from a failure of methylation to adequately support dopamine-stimulated phospholipid methylation, leading to impaired gamma synchronization. Synchronization in response to dopamine can provide a molecular mechanism for attention, as information in participating neural networks is able to bind together to create cognitive experience involving multiple brain regions.

Cho and colleagues find patients with schizophrenia showed a reduction in induced gamma band activity in the dorsolateral prefrontal cortex compared to healthy control subjects during a behavioral task that is known to challenge cognitive control processes. Importantly, the induced gamma band activity was correlated with better performance in healthy subjects, and negatively correlated with higher disorganization symptoms in patients with schizophrenia. These findings help explain previous post-mortem evidence of disruptions in thalamofrontocortical circuits in these patients.

These findings tie together several different previously identified phenotypes into a unifying story. The ability to link phenotypes across translational research domains is paramount to understanding complex neuropsychiatric diseases like schizophrenia. Cho and colleagues provide an excellent example for connecting evidence from symptom rating scales with behavioral, neural systems and neurophysiological data. Although not specifically addressed by the authors, these data may have important implications for understanding the neural basis of thought disorder as well. Hopefully, these findings will provide a frame-work for examining more informed and specific phenotypes relevant to schizophrenia.

The article by Kargieman and colleagues further specifies the cellular mechanisms underlying the actions of clozapine in a model of pharmacologically induced cortical dysfunction. Separately, clozapine has been demonstrated to be capable of reducing or eliminating the complex behavioral and cognitive impairments elicited by acutely administered NMDA antagonists (Geyer et al., 2001; Idris et al., 2005; Lipina et al., 2005), and these cellular mechanisms shown by Kargieman et al. may represent the level of interaction between clozapine and phencyclidine-like drugs.

What is surprising from so many of these studies is the quality of the reversal of effects produced by clozapine, despite the fact that it (like most other antipsychotic drugs) has limited efficacy both at an individual and population level. Furthermore, there remain many reports in the literature demonstrating that while some cognitive and symptomatic domains in schizophrenia are improved by clozapine, others clearly are not (Goldberg and Weinberger, 1996; Bilder et al., 2002). Why, then, is clozapine so effective in the PCP model? One concern, of course, is that its effects are related to a specific type of pharmacological interaction; one certainly needs to see clozapine's effects in other models that do not involve the acute administration of an NMDA antagonist.

Notably, several groups have been studying the effects of long-term administration of phencyclidine, sometimes followed by washout of the NMDA antagonist, to develop an alternative type of model that may depend upon the neuroadaptations resulting from blockade of NMDA receptors, rather than on the acute pharmacological action itself (Jentsch et al., 1997; Balla et al., 2003; Amitai et al., 2007, and many others). Whilst the specific validity of any one of these approaches is debatable, what appears to be clear is that the ability of clozapine to reverse behavioral or neurochemical deficits is much more tenuous. Is this a weakness of these models, or does it mean they are actually more realistic in their predictions?

Based upon these facts, one is left with a number of questions. First, is a model that predicts that clozapine is completely effective at blocking psychopathology or pathophysiology valid? Second, is the action of clozapine in any one model based upon one kind of manipulation really that provocative? And finally (and perhaps most importantly), is developing models that explain how clozapine works really in our best interest, or is it time to move beyond models that predict marginal gains from existing drugs, in order to look to targets flowing from the new valid genetic mechanisms that appear to hold the keys to the next generation of treatments for schizophrenia?

The paper by Kargieman et al. provides an interesting perspective on the effects of PCP on activity in the prefrontal cortex. Dr. Javitt brings up an excellent point in his commentary that the study highlights the importance of PCP in this preparation as a model of slow-wave sleep disturbances in schizophrenia. In anesthetized animals, field potential recordings resemble the up and down states observed in slow-wave sleep. These states are driven by NMDA receptors and, accordingly, NMDA antagonists such as PCP and ketamine should reduce them as reported. The odd thing about NMDA antagonists is that they themselves can be used as anesthetics to produce a state where slow delta oscillations predominate. For instance, robust up and down states or slow oscillations at or below delta are observed when ketamine is used as an anesthetic. Therefore, NMDA antagonists can induce a state where delta activity is prominent, yet if the subject is already in that state, the effect of the drug is to reduce such activity.

It may be a matter of perspective. In the awake state the cortex is highly desynchronized and firing is quite irregular with power in a variety of high-frequency bands and neurons firing at every phase angle of the field potential. With anesthetics, higher frequencies become unsustainable. Using realistic network model simulations, Durstewitz and Gabriel (2007) showed that if you come from a regime which is quite irregular and then reduce NMDA, you get clear delta wave oscillations, but as you keep reducing NMDA, these delta oscillations will become progressively reduced as well. So in the awake state, reductions in NMDA currents should relatively decrease power in many bands yet enhance delta, but if the network is already in delta, as when the subject is anesthetized or asleep, NMDA reduction would decrease power in this band. Therefore, the results of Kargieman et al., when viewed in light of the literature obtained in awake subjects and schizophrenics, confirms a non-trivial and somewhat paradoxical prediction of the NMDA theory of schizophrenia and the PCP model.

The results of this study are surprising. In those with schizophrenia, those on clozapine had by far the lowest relative risk of death (compared to patients on other antipsychotics). Compared to older medications, atypical antipsychotics, to date, do not seem to be impacting on the relative risk of death.

I congratulate the authors on this impressive study. The study is another reminder of the utility of population-based record linkage studies. Thank heavens for the Nordic countries' health registers.

A few years ago we wondered if the differential mortality rate for schizophrenia was worsening over time (Saha et al., 2007). In addition to differential access to health care, we worried that the adverse effects of atypical antipsychotics might be a “ticking time bomb” for worsening mortality in the decades to come. The new Finnish study shows a more nuanced picture emerging.

While the results are thought provoking, let’s not forget about the main game. We all agree that there is still much more work to be done in optimizing the general physical health of people with schizophrenia.

Clozapine: A First-Line Antipsychotic?
Tiihonen et al., of the University of Kuopio in Finland, compared mortality rates in over 66,000 patients with schizophrenia with the entire population of Finland and concluded that clozapine should be used as a first-line drug in the treatment of this disorder. Clozapine is a very effective antipsychotic, and for patients who have received it for several years, the improvement in clinical status can be quite remarkable (Lindstrom, 1988; Agid et al., 2008). Additionally, the improved mortality rate of patients on clozapine may be attributable, at least in part, to the close monitoring of their white blood cell count (WBC).

The stipulation that weekly or biweekly blood samples must be drawn is not an issue that can be viewed lightly, because approximately 1-2 percent of patients on clozapine may show significant decreases in their WBC. This may be a harbinger of agranulocytosis, a potentially lethal form of morbidity in which the bone marrow loses its ability to generate leukocytes; death remains a significant risk for patients taking this drug (Taylor et al., 2009). To some, this may seem like a small price to pay for an improved quality of life. For others, however, it represents an unacceptable degree of risk. Additionally, many patients consider the requirement for frequent blood drawing as intrusive and/or painful and refuse to have it done (personal observation).

Perhaps the greatest source of resistance to using clozapine as a “first-line” drug is the psychiatrist who is faced with this decision. In general, most believe that they would be exposing their patient to unnecessary risk and prefer to look toward other, more “benign” antipsychotic drugs (APDs) for treatment options. In practice, however, the second-generation atypical APDs are not necessarily better candidates for “first-line” use, because they may be even more likely to cause excessive weight gain, diabetes mellitus, and cardiovascular disease (Wehring et al., 2003; Henderson et al., 2005) and result in increased mortality (Meatherall and Younes, 2002). In addition to the risk of agranulocytosis, clozapine may also cause unacceptable amounts of sedation, drooling, and weight gain. Typical APDs, on the other hand, are associated with other side effects that can be quite debilitating. These include extrapyramidal movement disorders, such as 1) akathisia, a condition that may cause a worsening of symptoms as a result of agitation; 2) drug-induced Parkinsonism, in which hypokinesia usually complicates the negative symptoms of schizophrenia; and 3) tardive dyskinesia, a syndrome in which there are involuntary movements of the tongue and lips that can result in significant disability and even disfigurement (Peacock et al., 1996).

In considering the choice of an APD for a “first-episode” patient with schizophrenia, all of these factors must be considered. It is impossible to know how a particular patient with no prior history of having taken an APD will respond to any given drug. What may be an excellent “first-line” drug for one patient may not be so for another. So, the choice of a “first-line” drug requires that the doctor and patient work together to identify the APD that is most appropriate at a particular time in the course of the illness, particularly if the patient has a treatment-sensitive or treatment-resistant form of schizophrenia (Wang et al., 2004).

Lindstrom LH. The effect of long-term treatment with clozapine in schizophrenia: a retrospective study in 96 patients treated with clozapine for up to 13 years. Acta Psychiatr Scand. 1988;77:524-9. Abstract

Dr. Benes notes that clozapine is "...a very effective antipsychotic, and...improvement in clinical status can be quite remarkable." The mortality figures reported by Tihonen et al. have proved quite striking to schizophrenia researchers. The perception within the psychiatry community that clozapine is too risky for first-line therapy needs further assessment and discussion. Only about 5 percent of schizophrenics in the U.S. receive clozapine (Lieberman, 2009), leaving the vast majority of patients undermedicated because of this perception. The major issue with starting a patient on clozapine is WBC monitoring. I would like to call upon the NIMH to establish a major study in which schizophrenics are introduced to clozapine on an inpatient basis for 30-60 days to establish safety. It is well known that most WBC events associated with clozapine occur in the first few weeks of treatment. Also, I note that current prescribing practice with clozapine actually allows for monthly blood monitoring after 12 months of continuous clozapine use. Thus, the burden of monitoring diminishes sharply after one year.

When we look at an EEG recording, we see the summed electrical fields from throughout the brain that are manifestations of various kinds of information processing. Ideally, we would like to understand the actual information processing mechanisms that are manifested by these various neurophysiological phenomena, such as transients (event-related potential components) and oscillations. One of the reasons why there has been such interest in brain oscillations in recent years is that the neural mechanisms underlying some oscillations have been determined to a certain extent. For example, cortical gamma oscillations are generated through the synergistic interactions between pyramidal cells and fast-spiking inhibitory interneurons, whereas some beta oscillations are generated by gap junction-mediated interactions between only pyramidal cells (Kopell et al., 2010). Understanding the relationships between oscillations and the cognitive processes with which they are associated is an important goal of cognitive neuroscience. Furthermore, understanding these relationships would facilitate the use of EEG phenomena such as oscillations as biomarkers of particular cognitive functions that could be useful targets for treatments of neuropsychiatric disorders.

The study by Yamamoto et al. (Yamamoto et al., 2014) represents an advance in revealing the neural mechanisms underlying cognitive functions. Previous work by Tonegawa and colleagues has demonstrated that for mice, spatial working memory is subserved in part by interactions between the entorhinal cortex and the hippocampus (Suh et al., 2011; Kitamura et al., 2014). They found that these interactions occur in a circuit from the upper layers of the medial entorhinal cortex (MEC) to the dorsal CA1 region of the hippocampus and back to layer 5 of the MEC. In this study, Yamamoto et al. identified oscillatory synchronization in the high gamma band (65-120 Hz) between the MEC and CA1 as the mechanism underlying the apparently conscious retrieval of information in working memory. They were able to reach this conclusion not just by observing relationships between behavioral performance and oscillations, but also by manipulating circuit elements with state-of-the-art genetic and optogenetic methods to determine the causal relationships between neural activity in the MEC and CA1.

While this particular information processing mechanism may not be useful as a biomarker in schizophrenia research (as electrophysiological responses in the medial temporal lobe are difficult to detect with non-invasive methods), one can imagine using a similar set of approaches to study, for example, the beta synchronization between prefrontal and parietal cortices that is involved in visual working memory (e.g., Salazar et al., 2012). Knowledge of the exact circuit elements and direction of information flow within this prefrontal-parietal circuit, along with the cognitive function that arises from activity within it, could enable researchers to precisely model its disruption in schizophrenia and determine how best to ameliorate this dysfunction. We are likely to see more such efforts in the future.